TF-IDF: The on-page optimization approach your blog needs

TF-IDF is a calculation used by Google to understand the importance of terms on the pages of a website. Knowing this model helps you understand the search engine, but also helps you plan and optimize your content. Now understand what TF-IDF is and how it can help with on-page SEO.

Camila Casarotto

Oct 4, 21 | 10 min read
TF-IDF
Reading time: 8 minutes
The SEO market is becoming more and more mature. Gone are the days when it was enough to fill the page with keywords to reach the top of the search results. Google’s algorithm has already evolved to be able to make sense of the words in a text and even interpret the search intentions of users.

Do you think that with all that search engine

intelligence, it is possible to do just the basics of SEO to get results?

SEO professionals must understand how the algorithm thinks and adopt optimization approaches that meet its expectations in order to achieve good results in positioning. And these optimizations are becoming more and more sophisticated.

This is the case, for example, of what we will discuss in this article: TF-IDF, an on-page optimization approach . This acronym represents a way in which Google statistically determines the importance of a keyword or phrase by analyzing hundreds or thousands of documents.

By understanding the intelligence behind this search engine tool, you can adopt better on-page SEO strategies and stand out from the competition.

In this text you will learn:

What is TF-IDF?

When to use TF-IDF optimization?
What is TF-IDF?
TF-IDF is a statistical calculation adopted mali email list 100000 contact leads by Google’s algorithm to measure which terms are most relevant to a topic by analyzing how frequently they appear on a page, compared to their frequency on a larger set of pages.

TF-IDF is not a concept exclusive to SEO. It is used in different information retrieval systems. These include Internet search engines, but also library and text mining systems, for example.

The calculation serves as a term weighting factor , that is, to understand the importance of a specific term or phrase for a given document.

But, since you read the title of this article, you must be wondering: TF- what? So, let’s understand what this acronym stands for.

TF-IDF stands for Term Frequency – Inverse Document Frequency . This expression can be translated into Spanish as “Term Frequency – Inverse Document Frequency”. It’s still not very clear, right? So, let’s take it one step at a time.

TF stands for “term frequency.” That part of the calculation answers the question: How often does the term appear in this document? The higher the frequency of the term in the document, the higher its importance .

On the other hand, IDF stands for “inverse

document frequency”. In this part, the tool answers: How often does the term appear across all documents in the collection? The higher the frequency in documents, the lower the importance of the term .

The IDF calculation takes into account which terms are frequently repeated in texts, such as articles and conjunctions (the, the, it, and, but, that, etc.), and are not relevant to the documents. Thus, in the case of Google, neither for indexing nor for positioning.

Therefore, when the IDF factor is incorporated, the calculation decreases the weight of terms that occur very frequently in the set of documents and increases the weight of terms that appear more rarely. This diagram will help you understand it better.

semrush
Source: SEMrush
We are not going to go into the details of statistical calculations ( here you can understand the formulas ).

But we can sum it up like this: the importance of the term (TF-IDF value) increases according to the number of times the word appears in the how to organize a type of business events that impact document (TF) . But it is compensated by the number of repetitions in the document collection (IDF), which serves to adjust for the fact that some words appear more frequently overall.

How does Google use the TF-IDF calculation?
In Google’s case, the TF-IDF calculation helps the search engine emphasize the terms and phrases in the content of sites and blogs that really bz lists matter for indexing and ranking.